Skill Extraction with Behavioral Priors: Methods that leverage behavioral priors utilize offline datasets of demonstrations to bias a policy towards the most likely skills in the datasets. While related closely to imitation learning, behavioral priors have been mostly applied to improve Reinforcement Learning. Behavioral priors learned through maximum likelihood latent variable models have been used for structured exploration in RL [1]}, to solve complex long-horizon tasks from sparse rewards [2]}, and regularize offline RL policies [3]}, [4]}, [5]}. While impressive, RL with data-driven behavioral priors does not generalize to new tasks efficiently, often requiring millions of environment interactions to converge to an optimal policy for a new task.
